Improved Performance of Motor Drive Using RBFNNBased Hybrid Reactive Power MRAS Speed Estimator

摘要：

Model Reference Adaptive System (MRAS) represents one of the most attractive and popular solutions for sensorless control of induction motor drives. However, the performance of this scheme deteriorates at low speed. A new method is described which considerably improves the performance of MRAS-based sensorless drives in low speed regions of operation. It is applied to a vector-controlled induction motor drive. This new technique uses Radius Basis Function Neural Network (RBFNN) to entirely replace the conventional Proptional- intergral (PI) adaptation mechanism of classical hybrid reactive power MRAS speed estimator. The simulation results show great improvement in the speed estimation performance at low speed.

关键词： induction motor hybrid reactive power MRAS Speed estimator Radius Basis Function Neural Network

作者: Jinfeng Xiao Biwen Li Xueyu Gong Yifa Sheng Jun Chai

作者单位: College of Electrical Engineering University of South China Hengyang,HuNan Province,China College of Mechanical Engineering University of South China Hengyang,HuNan Province,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）